Knowledge graph and inference: use cases in online financial market

Title: Knowledge graph and inference: use cases in online financial market

Abstract: While the knowledge graph is an active research field in machine learning community, this powerful tool is still less known to the people in the industry. In this talk, I will first introduce knowledge graph and inference techniques including the recent developments which combine with deep learning. Then I will talk about several use cases in online financial market: fraud/anomaly detection, lost contact discovery, intelligent search, name disambiguation and etc. I will also briefly mention how to build knowledge graph using neo4j from different data sources.

Bio: Wenzhe Li is a chief data scientist from puhuifinance.com, the fastest growing P2P company in China, where he is leading the data science team to work on big data projects. Before joining puhui, he was a machine learning PhD at USC (on-leave) and a visiting student at University of Amsterdam. Even before that, he was a software engineer at Amazon and Goldman Sachs. He has also authored several papers from top-tier conferences including AAAI, KDD, CHI and etc.

Agenda:

Door opens at 6pm

6 pm - 6:30 pm networking + Light dinner

6:30 pm - 6:35 pm announcement and introduction

6:35 pm -- 7:35 pm main talk

7:35 pm -- 7:50 pm Q & A

7:50 pm -- 8:20 pm networking

8:20 pm -- 8:45 pm closing

9 pm office closed.